--Federated Learning (FL) is viewed as a promising technique for future distributed machine learning. It permits a large number of mobile devices participating in the training of a global model collaboratively without having to expose their local private data. Although the challenge of the network connection will be much relieved in 5G/B5G era, the training latency is still an obstacle preventing FL from being largely adopted. One of the most fundamental problems that leads to large training latency is the bad candidate-selection of FL participants. T o the best of our knowledge, the existing candidate-selection algorithms belong to the reactive manner . Under such reactive selection, the FL parameter server only knows the currently-observed resources of all candidates. In the dynamic FL environment, the mobile devices selected by the reactive candidate-selection algorithms very possibly fail to complete the training and reporting phases of FL. T o this end, we study the proactive candidate-selection for FL in this paper . We first let each candidate device locally predict the qualities of both its training and reporting phases using the LSTM network. Then, the proposed candidate-selection algorithm is implemented by the Deep Reinforcement Learning (DRL) framework, which can adapt to the dynamically varying factors in the metropolitan edge computing environment. Finally, the real-world trace-driven experiments prove that the proposed proactive approach outperforms the existing reactive algorithms with respect to the ratio of valid participants and the test accuracy of the aggregated global FL model. Federated Learning (FL) [1], [2] is a branch of distributed machine learning that enables a group of distributed devices to train their individual local models using the local dataset. Thus, FL is a promising computing paradigm in our future intelligent life, especially under the fifth generation (5G) and the beyond (B5G) communications networks. For example, the FederatedAveraging (FedAvg) algorithm [1] can help mobile users predict the next-words when users are using the Google's GBoard [3] in their smartphones. To realize a large-scale federated learning framework, a number of challenges must be addressed.

Different from the above prior works focusing on the ML strategy or structure design for improving the communication performance, this letter proposes to employ the emerging communication technique, namely the device-to-device (D2D) communications (see, e.g., [10, 11]), to relieve the "straggler's dilemma" issue for improving the performance of distributed ML-model training. Recently, the D2D communications have been recognized as one key technique in fifth-generation (5G) and beyond cellular networks, in which wireless devices in close proximity can directly communicate with each other without going through cellular infrastructures such as base stations (BSs). Motivated by this, we propose a new D2D-enabled data sharing design for mobile edge learning, which allows edge devices to share their data samples over D2D communication links. By properly controlling the amounts of data samples exchanged, this design can not only adjust the computation loads at devices for enhancing the training speed, but also reshape the data distribution (if data samples at edge devices are non-IID) for enhancing the training accuracy. In particular, we aim to minimize the total delay for the ML-model training under fixed numbers of local and global iterations (for training), by optimizing the radio resource allocation for both D2D data sharing and distributed model training.

As AI applications for mobile devices become more prevalent, there is an increasing need for faster execution and lower energy consumption for neural model inference. Historically, the models run on mobile devices have been smaller and simpler in comparison to large state-of-the-art research models, which can only run on the cloud. However, cloud-only inference has drawbacks such as increased network bandwidth consumption and higher latency. In addition, cloud-only inference requires the input data (images, audio) to be fully transferred to the cloud, creating concerns about potential privacy breaches. We demonstrate an alternative approach: shared mobile-cloud inference. Partial inference is performed on the mobile in order to reduce the dimensionality of the input data and arrive at a compact feature tensor, which is a latent space representation of the input signal. The feature tensor is then transmitted to the server for further inference. This strategy can improve inference latency, energy consumption, and network bandwidth usage, as well as provide privacy protection, because the original signal never leaves the mobile. Further performance gain can be achieved by compressing the feature tensor before its transmission.

This tutorial paper focuses on the variants of the bottleneck problem taking an information theoretic perspective and discusses practical methods to solve it, as well as its connection to coding and learning aspects. The intimate connections of this setting to remote source-coding under logarithmic loss distortion measure, information combining, common reconstruction, the Wyner-Ahlswede-Korner problem, the efficiency of investment information, as well as, generalization, variational inference, representation learning, autoencoders, and others are highlighted. We discuss its extension to the distributed information bottleneck problem with emphasis on the Gaussian model and highlight the basic connections to the uplink Cloud Radio Access Networks (CRAN) with oblivious processing. For this model, the optimal trade-offs between relevance (i.e., information) and complexity (i.e., rates) in the discrete and vector Gaussian frameworks is determined. In the concluding outlook, some interesting problems are mentioned such as the characterization of the optimal inputs ("features") distributions under power limitations maximizing the "relevance" for the Gaussian information bottleneck, under "complexity" constraints.

-- We introduce for the first time the utilization of Long short - term memory (LSTM) neural network architectures for the compensation of fiber nonlinearities in digital coherent systems. We conduct numerical simulations considering either C - band or O - band transmission systems for single channel and multi - channel 16 - QAM modulation format with polarization multiplexing . A detailed analysis regarding the effect of the number of hidden units and the length of the word of sym bols that trains the LSTM algorithm and corresponds to the considered channel memory is conducted in order to reveal the limits of LSTM based receiver with respect to performance and complexity. The numerical results show that LSTM Neural Networks can be v ery efficient as post processors of optical receivers which clas sify data that have undergone non - linear impairments in fiber and provide superior performance compared to digital back propagation, especially in the multi - channel transmission scenario. The complexity analysis shows that LSTM becomes more complex as the number of hidden units and the channel memory increase can be less complex than DBP in long distances ( 1000 km). There is a huge effort in fiber - optic communication industry to cope with the exponentially increasing capacity demands coming from next generation mobile networks and high bandwidth internet applica tions [1]. New trends such as internet of things especially in the context of tactile internet increase the requirements for real - time, high bandwidth, high availability connectivity in the access network domain, thus enhancing the capacity needs in metro and long - haul transmission networks. Optical fiber communication community predicted the imminent explosion of capacity needs ten years ago and started working intensively on techniques that can leverage fiber capabilities in this respect.

During CES 2019, Verizon announced the Built on 5G Challenge, a nationwide search for solid 5G innovations that could be commercialized using its low latency, high bandwidth millimeter wave 5G network. Today, the carrier announced three winners across the fields of AR, VR, and artificial intelligence, earning $1 million, $500,000, and $250,000 prizes, plus special 5G lab access to develop their projects. The million-dollar winner was Ario, developer of an AR safety and efficiency platform that lets workers "truly work from anywhere." Ario plans to use 5G to improve image recognition and overall performance using enhanced network speeds. Garou took second place with an ambitious VR project that represents the entire world as a 3D model, enabling users to collaboratively access content and enjoy social interactions within a highly complex virtual space.

BRUSSELS – EU countries could ban telecom operators deemed a security risk from critical parts of 5G infrastructure under guidelines issued Wednesday, amid U.S. pressure to shut out Chinese giant Huawei. The EU plan, which closely mirrors rules set out Tuesday by Britain allowing a limited role for Huawei, stops short of barring the company from the next-generation communications network designed for near-instantaneous data transfers. It leaves member states with the responsibility to ensure the safe rollout of 5G and warns them to screen operators carefully, saying security of the network will be critically important for the entire EU. The "toolbox" outlined by the European Commission avoids naming Huawei and does not call for an outright ban on any supplier. But it urges countries to "assess the risk profile of suppliers (and) … apply relevant restrictions for suppliers considered to be high risk" accordingly, including shutting them out of "key assets defined as critical and sensitive. It also recommends EU states avoid "major dependency on a single supplier" and "dependency on suppliers considered to be high risk.

Telecoms have evolved quite well with the times and are likely to continue to do so. The coming years will present the biggest technology trends, and telecoms will have to navigate their benefits and challenges in 2020 and beyond. Whichever side of the tracks a telecommunications business emerges as a result of upcoming technology trends, good or bad, you can be certain that a telco revolution is at hand. From 5G to AI and beyond, telcos will be met with a new world that creates more industries to service. This new world will open doors to markets that once had no need for telecoms, creating many new opportunities.

Britain has decided to allow Chinese tech giant Huawei to supply new high-speed network equipment, dealing a setback to the U.S. government and its global campaign to press allies into banning the company. The government's decision on Tuesday is the first by a major U.S. ally on the issue, which has seen intense lobbying from the Trump administration and China as the two vie for technological dominance. The British government said it is excluding "high risk" companies from supplying the sensitive "core" parts of the new fifth-generation, or 5G, networks. The core is the brain that keeps track, among other things, of smartphones connecting to networks and helps manage data traffic. But Britain will allow high risk suppliers to provide up to 35% of the less risky radio access network of antennas and base stations.

LONDON – Britain decided Tuesday to allow Chinese tech giant Huawei to supply new high-speed network equipment, ignoring the U.S. government's warnings that it would sever intelligence cooperation if the company was not banned. Britain's decision is the first by a major U.S. ally in Europe, and follows intense lobbying from the Trump administration and China as the two vie for technological dominance. It sets up a diplomatic clash with the Americans, who claim that British sovereignty is at risk because the company could give the Chinese government access to data, an allegation Huawei denies. "We would never take decisions that threaten our national security or the security of our Five Eyes partners," Foreign Secretary Dominic Raab said, referring a security arrangement in which Britain, the United States, Australia, Canada and New Zealand, share intelligence. "We know more about Huawei and the risks that it poses than any other country in the world."